The Ubiquitous p value: An Exploration of the Survey Sampling Methods Associated with p values in Medical Education Research.
Bibliographic record
Abstract
This study explored trends in the use of p values in medical education research studies among articles published in Academic Medicine (and its predecessors) between 1926 and 2023. Three hundred articles meeting inclusion criteria were analyzed for respondent selection method and the use of p values. Since p values describe the probability that a study’s findings are due to sampling error, only studies that used probability sampling should have reported p values. However, 65% of articles that used an attempted census (all members of the population were invited to participate) or non-probability sampling used p values to describe their findings. Stricter peer reviews and more education with respect to what p values mean, and their inappropriate use with non-probability sampling is needed to ensure the collection of accurate, valid, and useable survey data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.564 | 0.881 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".